{"id":"W3112688606","doi":"10.1038/s41467-021-26216-9","title":"Annotation-efficient deep learning for automatic medical image segmentation","year":2021,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"AI in cancer detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Cancer Institute; Youth Innovation Promotion Association; Recruitment Program of Global Experts; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Computer science; Annotation; Segmentation; Artificial intelligence; Deep learning; Machine learning; Imperfect","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002224695,0.00131736,0.0009420207,0.001628422,0.0005443346,0.001217475,0.002261157,0.001977192,0.002977615],"category_scores_gemma":[0.006697589,0.0007571646,0.0008380193,0.001444206,0.000879393,0.001436898,0.002621792,0.001717139,0.00191331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148336,"about_ca_system_score_gemma":0.002119156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004325616,"about_ca_topic_score_gemma":0.008477857,"domain_scores_codex":[0.9986196,0.0004181767,0.00007687344,0.0003984196,0.0003310518,0.0001558884],"domain_scores_gemma":[0.9980668,0.000746786,0.0002218582,0.0004980288,0.0003682889,0.00009817407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006955214,0.0002472337,0.003897118,0.0005871569,0.0002200661,0.0002666975,0.000352736,0.2223931,0.05880898,0.009661143,0.0219093,0.680961],"study_design_scores_gemma":[0.00003428004,0.00007248999,0.001028511,0.00005245759,0.00003392806,0.0001806821,0.00004579461,0.9475013,0.0302833,0.01333268,0.007404909,0.00002961656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02652934,0.001048157,0.9579863,0.0005192736,0.0000750265,0.0001153843,0.001044851,0.01063229,0.002049432],"genre_scores_gemma":[0.3176312,0.0007562003,0.6691478,0.0006098789,0.000106626,0.0003182679,0.005605027,0.001107006,0.004718022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004325616,"threshold_uncertainty_score":0.01176548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927901963085485,"score_gpt":0.3411973967394062,"score_spread":0.3219183771085513,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}